A Meta-Learning Approach to Methane Concentration Value Prediction

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 613)

Abstract

A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.

Keywords

Meta-learning Algorithm selection Stream data analysis Prediction 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland

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